Set Up
library(tidyverse)
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## ✔ purrr 1.2.1
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library(broom)
library(umap)
## Warning: package 'umap' was built under R version 4.5.3
library(plotly)
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## Attaching package: 'plotly'
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## last_plot
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## filter
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## layout
employed <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-02-23/employed.csv")
## Rows: 8184 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): industry, major_occupation, minor_occupation, race_gender
## dbl (3): industry_total, employ_n, year
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
1 Convert data to standardized form
employed_tidy <- employed_grouped <- employed %>%
filter(!is.na(employ_n)) %>%
group_by(occupation = paste(industry, minor_occupation), race_gender) %>%
summarise(n=sum(employ_n)) %>%
ungroup()
## `summarise()` has grouped output by 'occupation'. You can override using the
## `.groups` argument.
employed_tidy
## # A tibble: 1,434 × 3
## occupation race_gender n
## <chr> <chr> <dbl>
## 1 Agriculture and related Construction and extraction occup… Asian 2 e3
## 2 Agriculture and related Construction and extraction occup… Black or A… 6 e3
## 3 Agriculture and related Construction and extraction occup… Men 7.1 e4
## 4 Agriculture and related Construction and extraction occup… TOTAL 7.3 e4
## 5 Agriculture and related Construction and extraction occup… White 6.30e4
## 6 Agriculture and related Construction and extraction occup… Women 2 e3
## 7 Agriculture and related Farming, fishing, and forestry oc… Asian 8 e4
## 8 Agriculture and related Farming, fishing, and forestry oc… Black or A… 1.96e5
## 9 Agriculture and related Farming, fishing, and forestry oc… Men 4.53e6
## 10 Agriculture and related Farming, fishing, and forestry oc… TOTAL 5.74e6
## # ℹ 1,424 more rows
employed_pct <- employed_tidy <- employed_grouped %>%
#remove total category
filter(race_gender != "TOTAL") %>%
#add total column
left_join(employed_grouped %>%
filter(race_gender == "TOTAL") %>%
select(occupation, total = n)) %>%
# get pct in total
mutate(pct = n / total) %>%
filter(total > 1000) %>%
select(-n)
## Joining with `by = join_by(occupation)`
employed_tidy
## # A tibble: 1,160 × 4
## occupation race_gender total pct
## <chr> <chr> <dbl> <dbl>
## 1 Agriculture and related Construction and extractio… Asian 7.3 e4 0.0274
## 2 Agriculture and related Construction and extractio… Black or A… 7.3 e4 0.0822
## 3 Agriculture and related Construction and extractio… Men 7.3 e4 0.973
## 4 Agriculture and related Construction and extractio… White 7.3 e4 0.863
## 5 Agriculture and related Construction and extractio… Women 7.3 e4 0.0274
## 6 Agriculture and related Farming, fishing, and fore… Asian 5.74e6 0.0139
## 7 Agriculture and related Farming, fishing, and fore… Black or A… 5.74e6 0.0342
## 8 Agriculture and related Farming, fishing, and fore… Men 5.74e6 0.789
## 9 Agriculture and related Farming, fishing, and fore… White 5.74e6 0.911
## 10 Agriculture and related Farming, fishing, and fore… Women 5.74e6 0.211
## # ℹ 1,150 more rows
employed_standard <- employed_tidy %>%
#standardize
group_by(race_gender) %>%
mutate(pct = pct %>% scale() %>% as.numeric()) %>%
ungroup() %>%
#remove outliers
mutate(total = total %>% log() %>% scale() %>% as.numeric())
employed_standard
## # A tibble: 1,160 × 4
## occupation race_gender total pct
## <chr> <chr> <dbl> <dbl>
## 1 Agriculture and related Construction and extractio… Asian -1.30 -0.539
## 2 Agriculture and related Construction and extractio… Black or A… -1.30 -0.405
## 3 Agriculture and related Construction and extractio… Men -1.30 1.31
## 4 Agriculture and related Construction and extractio… White -1.30 0.725
## 5 Agriculture and related Construction and extractio… Women -1.30 -1.30
## 6 Agriculture and related Farming, fishing, and fore… Asian 0.819 -0.928
## 7 Agriculture and related Farming, fishing, and fore… Black or A… 0.819 -1.21
## 8 Agriculture and related Farming, fishing, and fore… Men 0.819 0.510
## 9 Agriculture and related Farming, fishing, and fore… White 0.819 1.38
## 10 Agriculture and related Farming, fishing, and fore… Women 0.819 -0.503
## # ℹ 1,150 more rows
4 select optimal number of clusters
kclusts <- tibble(k = 1:9) %>%
mutate(kclust = map(.x =k, .f = ~ kmeans(occupation_demo_tbl %>% select(-occupation), centers = .x, nstart = 20)),
glanced = map(.x =kclust, .f = glance))
kclusts %>%
unnest(glanced) %>%
ggplot(aes (k, tot.withinss)) +
geom_point() +
geom_line()
final_cluster <- kmeans(occupation_demo_tbl %>% select(-occupation), centers = 5, nstart = 20)
augment(final_cluster, occupation_demo_tbl) %>%
ggplot(aes(total, asian, color = .cluster)) +
geom_point()
Reduce dimension using UMAP
umap_results <- occupation_demo_tbl %>%
select(-occupation) %>%
umap()
umap_results_tbl <- umap_results$layout %>%
as.tibble() %>%
bind_cols(occupation_demo_tbl %>% select(occupation))
## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
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umap_results_tbl
## # A tibble: 232 × 3
## V1 V2 occupation
## <dbl> <dbl> <chr>
## 1 -7.04 -5.21 Agriculture and related Construction and extraction occupations
## 2 0.823 7.03 Agriculture and related Farming, fishing, and forestry occupati…
## 3 -3.28 -4.44 Agriculture and related Installation, maintenance, and repair o…
## 4 9.58 -4.73 Agriculture and related Manage-ment, business, and financial op…
## 5 1.55 7.07 Agriculture and related Management, business, and financial ope…
## 6 1.14 -5.84 Agriculture and related Office and administrative support occup…
## 7 -3.00 -4.27 Agriculture and related Production occupations
## 8 -0.860 -4.67 Agriculture and related Professional and related occupations
## 9 -6.67 -5.14 Agriculture and related Protective service occupations
## 10 -6.40 -5.10 Agriculture and related Sales and related occupations
## # ℹ 222 more rows
umap_results_tbl %>%
ggplot(aes(V1, V2)) +
geom_point()
Visualize clusters by adding k-means results
kmeans_map_tbl <- final_cluster %>%
augment(occupation_demo_tbl) %>%
select(occupation, .cluster) %>%
#add umap results
left_join(umap_results_tbl) %>%
#add employment info
left_join(employed_tidy %>%
select(-total) %>%
pivot_wider(names_from = race_gender, values_from = pct) %>%
janitor::clean_names())
## Joining with `by = join_by(occupation)`
## Joining with `by = join_by(occupation)`
kmeans_map_tbl
## # A tibble: 232 × 9
## occupation .cluster V1 V2 asian black_or_african_ame…¹ men white
## <chr> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Agriculture… 5 -7.04 -5.21 0.0274 0.0822 0.973 0.863
## 2 Agriculture… 4 0.823 7.03 0.0139 0.0342 0.789 0.911
## 3 Agriculture… 5 -3.28 -4.44 0.0155 0.0309 0.985 0.918
## 4 Agriculture… 5 9.58 -4.73 0.00992 0.00794 0.739 0.967
## 5 Agriculture… 4 1.55 7.07 0.00997 0.00882 0.741 0.962
## 6 Agriculture… 5 1.14 -5.84 0.0233 0.0155 0.159 0.938
## 7 Agriculture… 5 -3.00 -4.27 0.0332 0.104 0.815 0.820
## 8 Agriculture… 5 -0.860 -4.67 0.0339 0.0373 0.675 0.902
## 9 Agriculture… 5 -6.67 -5.14 0 0.0682 0.864 0.875
## 10 Agriculture… 5 -6.40 -5.10 0 0.0213 0.585 0.968
## # ℹ 222 more rows
## # ℹ abbreviated name: ¹black_or_african_american
## # ℹ 1 more variable: women <dbl>
g <- kmeans_map_tbl %>%
# Create text label
mutate(text_label = str_glue("Occupation: {occupation}
Cluster: {.cluster}
Asian: {asian %>% scales::percent(1)}
Women: {women %>% scales::percent(1)}")) %>%
# Plot
ggplot(aes(V1, V2, color = .cluster, text = text_label)) +
geom_point()
g %>% ggplotly(tooltip = "text")
